1. Field of the Invention
The present invention relates to an apparatus of generating a pattern recognition dictionary, a method thereof, a pattern recognition apparatus and a method thereof.
2. Description of the Related Art
In “Pattern Matching Method with Local Structure” by Kenichi Maeda and Sadaichi Watanabe, Shingakuron (D), Vol. J68-D, No. 3, pp. 345-352, 1985 and Jpn. Pat. Appln. KOKAI Publication No. 11-265452 are there proposed such a pattern recognition method as called a mutual subspace method which has been developed as an extension of subspace methods of “Pattern recognition Theory” by Taizo Iijima, Morikita Syuppan (1989) and “Subspace Method of Pattern recognition” by Erkki Oja, Research Studies Press.
In a mutual subspace method, first a dictionary pattern distribution is represented as a subspace to generate a dictionary subspace beforehand. The subspace can be generated by obtaining base vectors for that. Then, an input pattern distribution, which is a recognition object, is represented as a subspace, to generate an input subspace. Next, a minimum canonical angle is obtained which is formed between the input subspace and each of the dictionary subspaces generated beforehand, to decide that the recognition object belongs to a category which corresponds to such a dictionary subspace as to provide a smallest value of the minimum canonical angle. Belonging to a category means that, for example, in the case of a human face to be recognized using an image, “a person subject to recognition presently is already registered in the relevant dictionary”.
The mutual subspace method represents both an input side and a dictionary side as a subspace and so is superior to the subspace method in capacity of pattern deformation absorption. However, the mutual subspace method does not take into account a relationship with respect to other categories in representation, thus having such a problem that, for example, face recognition is liable to be influenced by illumination conditions.
Therefore, a method called a “constrained mutual subspace method” for preparing beforehand a “constraint subspace” constituted of essential features necessary in discrimination and projecting a comparison-subject subspace onto a constraint subspace and then applying a mutual subspace method is proposed in “Face Recognition under Variable Condition with Constrained Mutual Subspace Method—Learning of Constraint Subspace to Reduce Influence of Lighting Changes—” by Kazuhiro Fukui, Osamu Yamaguchi, Kaoru Suzuki, and Kenichi Maeda, Electronic Information Communication Society Literature Journal (D-II), Vol. J82-D-II, No. 4, pp. 613-620, 1999 and Jpn. Pat. Appln. KOKAI Publication 2000-30065.
In the constrained mutual subspace method, as shown in FIG. 7, subspaces P′ and Q′ which have been obtained by projecting comparison-subject subspaces P and Q onto a constraint subspace L are compared to each other by the mutual subspace method. Since the subspaces P′ and Q′ inherit essential features of the subspaces P and Q respectively, a difference between the subspaces P′ and Q′ consists of an essential portion which is extracted from differences (which are represented as a vector d but typically as a difference space) between the subspaces P and Q. Therefore, the present method has an advantage in being superior to the mutual subspace method in capacity of absorbing pattern deformation.
In a conventional constrained mutual subspace method, a constraint subspace is generated procedure-wise from an aggregate of difference subspaces which represent a difference between two subspaces.
In the procedure-wise generation method, first a difference subspace is generated for each of all combinations of subspaces which belong to the same category, to generate a first variation subspace from a principal component subspace of every difference subspace. The “subspaces which belong to the same category” refer to “subspaces generated from a face image of the same person” if exemplified in face recognition which uses an image. Next, a difference subspace is obtained for each of all combinations of subspaces which belong to a different category, to generate a second variation subspace from a principal component subspace of every difference subspace thus obtained.
The first variation subspace thus obtained corresponds to a “variation which occurs upon photographing of the same person under different conditions (expression, illumination, etc.)” in the case of face recognition by use of an image and so may be referred to as a space obtained by extracting components which are hopefully suppressed as much as possible in discrimination. The second variation subspace, on the other hand, corresponds to a “difference from others” in the case of face recognition by use of an image and so may be referred to as a space obtained by extracting components which are hopefully taken into account in discrimination.
Therefore, both the orthogonal complement of a space of the first variation subspace and the second variation subspace are a space obtained by extracting components which are hopefully taken into account in discrimination, so that a portion which is common to these is calculated as a constraint subspace.
By the above-mentioned procedure-wise method for generating a constraint subspace, however, it is necessary to generate a difference subspace for all of the combinations of subspaces. If there are m number of subspaces, it is necessary to calculate a difference subspace mC2 number of times. Accordingly, as the number of categories increases, the number of difference subspaces to be obtained becomes abundant, thus bringing about a problem of a decrease in processing speed.